14 papers
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
Hengyuan Zhang, Zhihao Zhang, Mingyang Wang +26
Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat…
PoeTone: A Framework for Constrained Generation of Structured Chinese Songci with LLMs
Zhan Qu, Shuzhou Yuan, Michael Färber
This paper presents a systematic investigation into the constrained generation capabilities of large language models (LLMs) in producing Songci, a classical Chinese poetry form cha…
Analyzing Bias in False Refusal Behavior of Large Language Models for Hate Speech Detoxification
Kyuri Im, Shuzhou Yuan, Michael Färber
While large language models (LLMs) have increasingly been applied to hate speech detoxification, the prompts often trigger safety alerts, causing LLMs to refuse the task. In this s…
Beyond Over-Refusal: Scenario-Based Diagnostics and Post-Hoc Mitigation for Exaggerated Refusals in LLMs
Shuzhou Yuan, Ercong Nie, Yinuo Sun +3
Large language models (LLMs) frequently produce false refusals, declining benign requests that contain terms resembling unsafe queries. We address this challenge by introducing two…
Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models
Ercong Nie, Shuzhou Yuan, Bolei Ma +4
Probing the multilingual knowledge of linguistic structure in LLMs, often characterized as sequence labeling, faces challenges with maintaining output templates in current text-to-…
The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Konrad Löhr, Shuzhou Yuan, Michael Färber
Large Language Models (LLMs) are increasingly integral to information dissemination and decision-making processes. Given their growing societal influence, understanding potential b…